Fully automatic hybrid registration method based on point feature detection without user intervention

Bang Bon Koo, Jong Min Lee, June Sic Kim, In Young Kim, Jun Soo Kwon, Sun I. Kim

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In earlier work (KIM, J.S, MBEC, 2003), we demonstrated the registration method with a non-linear transformation using intensity similarity and feature similarity. Although the former approach showed good match in global shape of brain and feature-defined region, method contains user interventions for defining appropriate and sufficient number features. While manual delineating the region of interests for sufficient number of feature is a very time-consuming and can provide intra-, inter-rater variability, we proposed fully automatic hybrid registration via automatic feature defining method. Automatic feature definition was performed on the cortical surface from CLASP (KIM, J.S, Neuroimage, 2005) with using cortical surface matching algorithm (Robbins, S., MIA, 2004) and then applied to hybrid registration. The object of this work is to develop fully automated hybrid registration method which reveals enhanced performance in comparison to previous automated registration methods. In the result, our proposed scheme showed efficient performance from maintaining the strong points of hybrid registration without any user intervention.

Original languageEnglish
Title of host publicationMedical Imaging 2006
Subtitle of host publicationImage Processing
Volume6144 II
StatePublished - 22 Jun 2006
EventMedical Imaging 2006: Image Processing - San Diego, CA, United States
Duration: 13 Feb 200616 Feb 2006


OtherMedical Imaging 2006: Image Processing
CountryUnited States
CitySan Diego, CA

Cite this

Koo, B. B., Lee, J. M., Kim, J. S., Kim, I. Y., Kwon, J. S., & Kim, S. I. (2006). Fully automatic hybrid registration method based on point feature detection without user intervention. In Medical Imaging 2006: Image Processing (Vol. 6144 II). [61442N] https://doi.org/10.1117/12.652935